9 papers
AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition
Yuqi Li, Yi-Cheng Lin, Xianglong Wang +5
On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for edge devices. Multi-teacher…
AgentAsk: Multi-Agent Systems Need to Ask
Bohan Lin, Kuo Yang, Zelin Tan +8
Multi-agent systems (MAS) built on large language models promise improved problem-solving through collaboration, yet they often fail to consistently outperform strong single-agent…
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Kuo Yang, Xingjie Yang, Linhui Yu +5
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction m…
Spatiotemporal Causal Decoupling Model for Air Quality Forecasting
Jiaming Ma, Guanjun Wang, Sheng Huang +4
Due to the profound impact of air pollution on human health, livelihoods, and economic development, air quality forecasting is of paramount significance. Initially, we employ the c…
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation
Jiayue Liu, Zhongchao Yi, Zhengyang Zhou +4
Discovering regularities from spatiotemporal systems can benefit various scientific and social planning. Current spatiotemporal learners usually train an independent model from a s…
Soft causal learning for generalized molecule property prediction: An environment perspective
Limin Li, Kuo Yang, Wenjie Du +3
Learning on molecule graphs has become an increasingly important topic in AI for science, which takes full advantage of AI to facilitate scientific discovery. Existing solutions on…
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization
Gengyu Lin, Zhengyang Zhou, Qihe Huang +3
Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions o…
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
Zhengyang Zhou, Qihe Huang, Binwu Wang +4
Spatiotemporal (ST) learning has become a crucial technique to enable smart cities and sustainable urban development. Current ST learning models capture the heterogeneity via vario…
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics
Zhengyang Zhou, Yang Kuo, Wei Sun +4
Spatiotemporal forecasting is an imperative topic in data science due to its diverse and critical applications in smart cities. Existing works mostly perform consecutive prediction…